An Evolutionary Many-Objective Algorithm Based on a Novel Tournament Selection Strategy

Abstract

Recent studies in scientific research and engineering practice have the tendency to employ evolutionary algorithms to solve multi-objective optimization problems (MOPs), which has a certain effect. In the evolutionary process, the mating selection that aims to make a good preparation for exchanging the information of individuals plays an important role in multi-objective evolutionary algorithms (MOEAs). However, existing MOEAs usually use random selection strategy to form the mating pools. This strategy of generating offspring has a certain randomness, which will affect the quality of offspring, thereby deteriorating the effectiveness of the algorithm. To address this issue, we propose a novel tournament selection strategy, in which a type of binary tournament selection strategy based on the grid dominance relation and density information is adopted to select individuals for variation. The experimental results indicate that the proposed method performed well in terms of convergence and diversity, especially due to the significant benefits of high-dimensional objective space handling.

Hyper-Spectral Image Pixel Classification Based on Golden Sine and Chaotic Spotted Hyena Optimization Algorithm

“…A common method is to use multiple models to predict data, then compare the predicted value with the true value, and select the optimal result as the final result. In practical applications, the band selection problem can be transformed into a mathematical programming problem, with the optimization goal of maximizing the objective function under given constraints [27]. The selection of frequency bands is relatively complex, and ordinary decimal programming usually cannot solve this discontinuous composite optimization problem.…”